torchdrivesim.traffic_controls
==============================

.. py:module:: torchdrivesim.traffic_controls

.. autoapi-nested-parse::

   Definitions of traffic controls. Currently, we support traffic lights, stop signs, and yield signs.



Classes
-------

.. autoapisummary::

   torchdrivesim.traffic_controls.BaseTrafficControl
   torchdrivesim.traffic_controls.TrafficLightControl
   torchdrivesim.traffic_controls.YieldControl
   torchdrivesim.traffic_controls.StopSignControl


Module Contents
---------------

.. py:class:: BaseTrafficControl(pos: torch.Tensor, allowed_states: Optional[List[str]] = None, replay_states: Optional[torch.Tensor] = None, mask: Optional[torch.Tensor] = None)

   Base traffic control class. All traffic controls are static rectangular stoplines with additional discrete state.
   States are advanced by calling `step` and can be set either from a given recorded history of states or
   by `compute_state` if current timestep exceeds the given recorded history.

   :param pos: BxNx5 stoplines tensor [x, y, length/thickness, width, orientation]
   :param allowed_states: Allowed values of state, e.g. colors for traffic lights.
   :param replay_states: BxNxT tensor of states to replay, represented as indices into `allowed_states`. Default T=0.
   :param mask: BxN boolean tensor indicating whether a given traffic control element is present and not a padding.


   .. py:attribute:: pos


   .. py:attribute:: allowed_states
      :value: None



   .. py:attribute:: replay_states
      :value: None



   .. py:attribute:: mask
      :value: None



   .. py:attribute:: corners


   .. py:attribute:: state


   .. py:method:: _default_allowed_states() -> List[str]
      :classmethod:



   .. py:method:: _default_replay_states() -> torch.Tensor


   .. py:method:: _default_mask() -> torch.Tensor


   .. py:method:: _default_state() -> torch.Tensor


   .. py:property:: total_replay_time
      :type: int



   .. py:method:: copy()

      Duplicates this object, allowing for independent subsequent execution.



   .. py:method:: to(device: torch.device)

      Modifies the object in-place, putting all tensors on the device provided.



   .. py:method:: extend(n: int, in_place: bool = True)

      Multiplies the first batch dimension by the given number.
      This is equivalent to introducing extra batch dimension on the right and then flattening.



   .. py:method:: select_batch_elements(idx: torch.Tensor, in_place: bool = True)

      Picks selected elements of the batch.
      The input is a tensor of indices into the batch dimension.



   .. py:method:: set_state(state: torch.Tensor) -> None

      Sets control state tensor.



   .. py:method:: compute_state(time: int) -> torch.Tensor

      Computes the state at a given time index. By default, it repeats the current state. Ignores replay states.



   .. py:method:: step(time: int) -> None

      Advances the state of the control given a time step. If the time value is within the range of replayable states
      then this state will be used otherwise the function `compute_state(time)` will be used to calculate a new state.



   .. py:method:: compute_violation(agent_state: torch.Tensor) -> torch.Tensor

      Given a collection of agents, computes which agents violate the traffic control.
      As deciding violations is typically complex, this function provides an approximate answer only.
      The base class reports no violations.

      :param agent_state: BxAx5 tensor of agents states - x,y,length,width,orientation

      :returns: BxA boolean tensor indicating which agents violated the traffic control



.. py:class:: TrafficLightControl(pos: torch.Tensor, allowed_states: Optional[List[str]] = None, replay_states: Optional[torch.Tensor] = None, mask: Optional[torch.Tensor] = None)

   Bases: :py:obj:`BaseTrafficControl`


   Traffic lights, with default allowed states of ['red', 'yellow', 'green'].
   An agent is regarded to violate the traffic light if the light is red and the agent
   bounding box substantially overlaps with the stop line.


   .. py:attribute:: violation_rear_factor
      :value: 0.1



   .. py:method:: _default_allowed_states() -> List[str]
      :classmethod:



   .. py:method:: compute_violation(agent_state: torch.Tensor) -> torch.Tensor

      Given a collection of agents, computes which agents violate the traffic control.
      As deciding violations is typically complex, this function provides an approximate answer only.
      The base class reports no violations.

      :param agent_state: BxAx5 tensor of agents states - x,y,length,width,orientation

      :returns: BxA boolean tensor indicating which agents violated the traffic control



.. py:class:: YieldControl(pos: torch.Tensor, allowed_states: Optional[List[str]] = None, replay_states: Optional[torch.Tensor] = None, mask: Optional[torch.Tensor] = None)

   Bases: :py:obj:`BaseTrafficControl`


   Yield sign, indicating that cross traffic has priority.
   Violations are not computed.


.. py:class:: StopSignControl(pos: torch.Tensor, allowed_states: Optional[List[str]] = None, replay_states: Optional[torch.Tensor] = None, mask: Optional[torch.Tensor] = None)

   Bases: :py:obj:`BaseTrafficControl`


   Stop sign, indicating the vehicle should stop and yield to cross traffic.
   Violations are not computed.